Many people with disabilities often struggle to interact with small UI content or comprehend small text displayed on mobile app user interfaces (UIs). To overcome these difficulties, they rely on scaling assistive services to adjust and increase the size of UI content. Unfortunately, recent studies have shown that a large number of mobile apps are not compatible with these services, leading to inconsistencies that can distort the layout of the UIs of these apps. These distortions make it even more troublesome for people with disabilities to use these mobile apps, which defeats the purpose of these scaling assistive services. Existing techniques are limited in terms of repairing these issues. In this paper, we present ScaleFix, a novel approach to automatically repair UI scaling accessibility issues in mobile apps. ScaleFix is the first-ever technique to repair such issues. The evaluation of ScaleFix on real-world Android apps demonstrated its ability to effectively repair UI scaling accessibility issues. Additionally, in a user study with individuals affected by these issues, the results demonstrated that ScaleFix was able to improve the accessibility of mobile UIs without negatively impacting the readability or aesthetics of the UI.

Patch prioritization ranks candidate patches based on their likelihood of being correct.The fixing ingredients that are more likely to be the fix for a bug, share a high contextual similarity.A recent study shows that combining both syntactic and semantic similarity for capturing the contextual similarity, can do better in prioritizing patches.In this study, we evaluate the impact of combining the syntactic and semantic features on patch prioritization using the Insertion mutation operators.This study inspects the result of different combinations of syntactic and semantic features on patch prioritization.As a pilot study, the approach uses genealogical similarity to measure the semantic similarity and normalized longest common subsequence, normalized edit distance, cosine similarity, and Jaccard similarity index to capture the syntactic similarity.It also considers Anti-Pattern to filter out the incorrect plausible patches.The combination of both syntactic and semantic similarity can reduce the search space to a great extent.Also, the approach generates fixes for the bugs before the incorrect plausible one.We evaluate the techniques on the IntroClassJava benchmark using Insertion mutation operators and successfully generate fixes for 6 bugs before the incorrect plausible one.So, considering the previous study, the approach of combining syntactic and semantic similarity can able to solve a total number of 25 bugs from the benchmark, and to the best of our knowledge, it is the highest number of bug solved than any other approach.The correctness of the generated fixes are further checked using the publicly available results of CapGen and thus for the generated fixes, the approach achieves a precision of 100%.